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<table width="100%"><tr><td>rent(gamlss.util)</td><td align="right">R Documentation</td></tr></table><object type="application/x-oleobject" classid="clsid:1e2a7bd0-dab9-11d0-b93a-00c04fc99f9e">
<param name="keyword" value="R:   rent">
<param name="keyword" value=" Rent data">
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<h2>Rent data</h2>


<h3>Description</h3>

<p>
A survey was conducted in April 1993 by Infratest Sozialforschung.
A random sample of accommodation with new tenancy agreements or
increases of rents within the last four years in Munich was
selected including: i) single rooms, ii) small apartments, iii)
flats, iv) two-family houses. Accommodation subject to price
control rents, one family houses and special houses, such as
penthouses, were excluded because they are rather different from
the rest and are considered a separate market. For the purpose of
this study, 1967 observations of the variables listed below were
used, i.e. the rent response variable R followed by the
explanatory variables found to be appropriate for a regression
analysis approach by Fahrmeir <EM>et al.</EM>  (1994, 1995):
</p>


<h3>Usage</h3>

<pre>data(rent)</pre>


<h3>Format</h3>

<p>
A data frame with 1969 observations on the following 9 variables.
<dl>
<dt>R</dt><dd>: rent response variable, the monthly net rent in DM, i.e.
the monthly rent minus calculated or estimated utility cost</dd>
<dt>Fl</dt><dd>: floor space in square meters</dd>
<dt>A</dt><dd>: year of construction</dd>
<dt>Sp</dt><dd>: a variable indicating whether the location is above 
average, <code>1</code>, (550 observations) or not, <code>0</code>, (1419 observations)</dd>
<dt>Sm</dt><dd>: a variable indicating whether the location is below, <code>1</code>,
average (172 obs.) or not, <code>0</code>, (1797 obs.)</dd>
<dt>B</dt><dd>: a factor with levels indicating whether there is a bathroom, <code>1</code>, (1925
obs.)  or not, <code>0</code>, (44 obs.) </dd>
<dt>H</dt><dd>: a factor with levels indicating whether there is central heating, <code>1</code>,
(1580 obs.)  or not, <code>0</code>, (389 obs.) </dd>
<dt>L</dt><dd>: a factor with levels indicating whether the kitchen equipment is
above average, <code>1</code>,  (161 obs.) or not, <code>0</code>, (1808 obs.) </dd>
<dt>loc</dt><dd>: a factor (combination of Sp and Sm) indicating whether the location is below, <code>1</code>, average,  <code>2</code>, or above average <code>3</code></dd>
</dl>

<h3>Details</h3>

<p>
This set of data were used by Stasinopoulos <EM>et al.</EM>  (2000) to fit a model where both the mean 
and the dispersion parameter of a Gamma distribution were modelled using the explanatory variables.
</p>


<h3>Source</h3>

<p>
Provide by Prof. L. Fahrmeir
</p>


<h3>References</h3>

<p>
Fahrmeir L., Gieger C., Mathes H. and Schneeweiss H. (1994)
Gutachten zur Erstellung des Mietspiegels fur Munchen 1994, Teil
B: Statistiche Analyse der Nettomieten. Hrsg: Landeshaupttstadt
Munchen, Sozialreferat-Amt fur Wohnungswesen.
</p>
<p>
Fahrmeir L., Gieger C., and Klinger, A.  (1995) Additive,
dynamic and multiplicative regression. In <EM> Applied Statistics:
Recent Developments</EM>, Vandenhoeck and Ruprecht, Gottingen.
</p>
<p>
Stasinopoulos, D. M., Rigby, R. A. and Fahrmeir, L., (2000), 
Modelling rental guide data  using mean and dispersion additive models, <EM>Statistician</EM>, <B>49 </B>,
479-493.
</p>
<p>
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
<EM>Journal of Statistical Software</EM>, Vol. <B>23</B>, Issue 7, Dec 2007, <a href="http://www.jstatsoft.org/v23/i07">http://www.jstatsoft.org/v23/i07</a>.
</p>


<h3>Examples</h3>

<pre>
data(rent)
attach(rent)
plot(Fl,R)
</pre>



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